How businesses can improve the customer experience with machine learning

Customer satisfaction is the bread and butter of every customer service team. We all want to make our customers happy, and satisfaction becomes both the reason we show up to work and barometer by which we measure our success. Every interaction offers a business the chance to leave a customer feeling good, helping to build a relationship for the longer term. Yet some customer interactions have a mercurial quality and end up in a different place than they began. If you knew what direction customer interactions were heading, you’d be better-equipped to handle them successfully, able to deploy specific tools and resources to ensure a positive outcome.

In a recent report, Aphrodite Brinsmead, Ovum’s Principal Analyst, Customer Engagement, notes that while companies employ many different metrics to gauge customer satisfaction—from task-resolution rates to callback rates—these metrics are typically reviewed after the fact and can’t help an agent avoid a difficult customer encounter before it happens.

This lack of foresight has far-reaching consequences, affecting brand reputation and customer retention. Today’s consumers expect simple and fast resolutions to their questions and problems. Increasingly, they’re reaching out to companies across myriad digital channels. With these and other factors at play, Brinsmead advocates a new approach to customer service: “Ideally, contact centers need to determine customer satisfaction in real time to militate against churn and negative sentiment being shared among peers.”

By applying machine learning and predictive analytics, companies can automate simple tasks, such as answering frequently asked questions, and revolutionize the way they provide customer support.

“Here at Zendesk, we’re applying machine learning capabilities to some of our products,” shared Adrian McDermott, senior vice president of product development at Zendesk. “Tools like Satisfaction Prediction and Automatic Answers give customers the resources they need to resolve issues faster, and to focus more time and attention on inquiries that require a human touch. As we move forward, we’ll continue to expand our use of machine learning in exciting new ways, by reading more content clues and offering recommended responses.”

The more expedient a resolution, the more satisfied customers are with your business. It’s a service and satisfaction cycle that has a real and measurable impact on a company’s bottom line.

Satisfaction Prediction
Zendesk’s Satisfaction Prediction tool offers companies flexibility and customization as they seek to solve their own unique customer service challenges. Based on a “satisfaction prediction” score assigned to each interaction, businesses can choose which messages to prioritize and can devote more time and resources to solving the most challenging issues. It’s like handing a crystal ball to every customer service agent on your team.

For example, by flagging negative satisfaction ratings as a predictor of high-risk customers, Pinterest can now mobilize customer service agents to proactively fix problems. Before machine learning tools, agent time was spent first discovering problems, then trying to solve them.

“Previously, we had a dedicated team member who would look through our tickets and escalate experiences identified as potentially negative,” shared Maggie Armato, reactive support lead at Pinterest. “Now we use the prediction score to accurately and automatically identify these types of tickets so our agents can focus on higher value areas.”

Automatic Answers
Zendesk also uses machine learning to provide automatic answers to common customer service queries, empowering customers to self-serve. Studies show that a high percentage of today’s consumers actually prefer to solve their own problem. By using built-in tools for real-time customer feedback, companies can continually refine their self-serve process, offering faster and more accurate information. Even small companies without a huge data set can turn what they know about their customers into a better experience.

In the future, as consumer products continue to interact with the Internet, digital channels will pump in even more data. Aided by machine learning, customer service centers will be in a position to analyze this data and react to a problem before the customer is even aware of the issue—truly an innovative step forward for companies focused on customer service.